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均值突变模型中的条件自助法
Bootstrap methods are not inherently conditional, but they can be made so by appropriate stratification of the simulated samples which bootstrap produces. We show how stratification can work in a bootstrap analysis of mean-shift in Nile river flow data. The results are compared with both parametric and semiparametric likelihood analyses. The paper ends with some general remarks on conditional bootstraps.
均值突变模型中的条件自助法
Bootstrap methods are not inherently conditional, but they can be made so by appropriate stratification of the simulated samples which bootstrap produces. We show how stratification can work in a bootstrap analysis of mean-shift in Nile river flow data. The results are compared with both parametric and semiparametric likelihood analyses. The paper ends with some general remarks on conditional bootstraps.
截尾刀切法
Summary The jackknifed version of a regular estimate is the average of the sample pseudo-values, which in general are not independent and often have long-tailed distributions. We examine the simple alternative of the trimmed average of the pseudo-values, which is a naive type of robust estimate. The asymptotic theory is developed and two simple examples are discussed.
预测似然
The likelihood function is the common basis of all parametric inference. However, with the exception of an ad hoc definition by Fisher, there has been no such unifying basis for prediction of future events, given past observations. This article proposes a definition of predictive likelihood which can help to remove some nonuniqueness problems in sampling-theory predictive inference, and which can produce a simple prediction analog of the Bayesian parametric result, posterior…
对称分布的估计
One estimate of a symmetric location cumulative distribution function F{x - θ) is obtained by symmetrizing the sample cumulative distribution function with respect to the estimated centre of symmetry θ. We prove an earlier conjecture that if θ is asymptotically efficient, then the symmetrized estimated cumulative distribution function is uniformly superior to the sample distribution function.